# LLM-Only PDDL Domain Repair with Open-Weight Models

arXiv: https://arxiv.org/abs/2608.17341 · HTML: https://mlsanity.com/paper/2608.17341/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 11 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 4 of 4)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models](https://mlsanity.com/paper/2503.18971/) (2025)
- [Planning in the Dark: LLM-Symbolic Planning Pipeline without Experts](https://mlsanity.com/paper/2409.15915/) (2024)
- [Chasing Progress, Not Perfection: Revisiting Strategies for End-to-End LLM Plan Generation](https://mlsanity.com/paper/2412.10675/) (2024)

## Abstract (arXiv)

AI planning is concerned with finding a sequence of actions that achieves a specified goal. It relies on explicit models of the world, commonly represented in the Planning Domain Definition Language (PDDL). An active line of research investigates how errors in such models can be detected and repaired. For example, users may provide positive test plans that are solutions, and negative test plans that fail during execution. Automated repair methods then modify the PDDL model to satisfy these constraints. In this paper, we evaluate the ability of recent open-weight large language models to perform this repair task using an LLM-only approach. Our experiments show that the symbolic baseline achieves an F1 score of .49, while the best-performing LLM reaches .87 with high reasoning effort, an absolute improvement of .38. However, that setting has a mean test pass rate of only .82, falling to .06 on the Thoughtful domain; even the best setting that includes the test traces reaches only .92. Thus, current open-weight models cannot guarantee satisfaction of the test constraints required for reliable automated model repair.